BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Eastern BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210727T015140Z UID:79725699-260F-48D9-8F2D-1535EFE9E463 DTSTART;TZID=Canada/Eastern:20210726T120000 DTEND;TZID=Canada/Eastern:20210726T130000 DESCRIPTION:Energy efficiency\, which has emerged as a top priority in clou d ecosystems\, is the outcome of appropriate pricing mechanisms and resour ce allocations. Static pricing mechanisms are the most dominant approach a mong all the others. They are simple to implement for the service provider s and easy to understand for the service users. Inaccurate price calculati on and low efficient resource allocation in static pricing mechanisms made researchers discover other solutions to overcome these issues. Double auc tion mechanisms are among the most appropriate dynamic models. The main ch allenge of conventional double auction mechanisms is not considering the c loud ecosystems' specifications\, such as dynamic online features. The ter m dynamic refers to the many variable parameters in cloud ecosystems\, and they constantly change. Conventional static offline pricing solutions are set based on a series of parameters before running the process. In dynami c online methods\, we customize our pricing models based on dynamic and cu rrent parameters. Also\, we continuously optimize these methods to attain optimal results. In this seminar\, firstly\, we define a Dynamic Online Do uble Auction Mechanism (DODAM) for the IaaS environment\, which covers a b roader range of IaaS parameters by considering the dynamic online features of such markets. Considering the features of cloud dynamic online ecosyst ems\, DODAM provides an appropriate price scheduling for service providers and service users. Cloud secondary market is a new paradigm in IaaS ecosy stems. In these markets\, brokers and reseller buyers have attained their resources from service providers of the cloud primary markets in the form of timed packages and repackage them into smaller chunks. As unsold packag es do not transfer to the next intervals\, brokers and reseller buyers nee d to sell their packages as much as possible. We develop a mechanical desi gn that includes a market-based pricing model and a resource allocation al gorithm in such markets as our second contribution. Next\, by formulating the inherent competitive features in cloud secondary markets\, we improve the pricing and resource allocation mechanisms in such competitive ecosyst ems. In the last contribution\, we proposed a Priority-based Dynamic Onlin e Double Auction Model (PB-DODAM)\, considering the perishability and time constraints of traded resources in IaaS secondary markets. The provided e xperimental results show that all proposed mechanisms drastically increase resource utilization and the overall utility.\n\nSpeaker(s): Dr. Reza Dib aj\, \n\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee. org/m/276942 LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o rg/m/276942 ORGANIZER:reza.dibaj@ieee.org SEQUENCE:1 SUMMARY:Sustainable Service Pricing in Cloud Ecosystems URL;VALUE=URI:https://events.vtools.ieee.org/m/276942 X-ALT-DESC:Description: <br /><p>Energy efficiency\, which has emerged as a top priority in cloud ecosystems\, is the outcome of appropriate pricing mechanisms and resource allocations. Static pricing mechanisms are the mos t dominant approach among all the others. They are simple to implement for the service providers and easy to understand for the service users. Inacc urate price calculation and low efficient resource allocation in static pr icing mechanisms made researchers discover other solutions to overcome the se issues. Double auction mechanisms are among the most appropriate dynami c models. The main challenge of conventional double auction mechanisms is not considering the cloud ecosystems' specifications\, such as dynamic onl ine features. The term dynamic refers to the many variable parameters in c loud ecosystems\, and they constantly change. Conventional static offline pricing solutions are set based on a series of parameters before running t he process. In dynamic online methods\, we customize our pricing models ba sed on dynamic and current parameters. Also\, we continuously optimize the se methods to attain optimal results. In this seminar\, firstly\, we defin e a Dynamic Online Double Auction Mechanism (DODAM) for the IaaS environme nt\, which covers a broader range of IaaS parameters by considering the dy namic online features of such markets. Considering the features of cloud d ynamic online ecosystems\, DODAM provides an appropriate price scheduling for service providers and service users. Cloud secondary market is a new p aradigm in IaaS ecosystems. In these markets\, brokers and reseller buyers have attained their resources from service providers of the cloud primary markets in the form of timed packages and repackage them into smaller chu nks. As unsold packages do not transfer to the next intervals\, brokers an d reseller buyers need to sell their packages as much as possible. We deve lop a mechanical design that includes a market-based pricing model and a r esource allocation algorithm in such markets as our second contribution. N ext\, by formulating the inherent competitive features in cloud secondary markets\, we improve the pricing and resource allocation mechanisms in suc h competitive ecosystems. In the last contribution\, we proposed a Priorit y-based Dynamic Online Double Auction Model (PB-DODAM)\, considering the p erishability and time constraints of traded resources in IaaS secondary ma rkets. The provided experimental results show that all proposed mechanisms drastically increase resource utilization and the overall utility.</p> END:VEVENT END:VCALENDAR